Journal of clinical monitoring and computing
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J Clin Monit Comput · Feb 2023
Randomized Controlled TrialPrediction of acute postoperative pain based on intraoperative nociception level (NOL) index values: the impact of machine learning-based analysis.
The relationship between intraoperative nociception and acute postoperative pain is still not well established. The nociception level (NOL) Index (Medasense, Ramat Gan, Israel) uses a multiparametric approach to provide a 0-100 nociception score. The objective of the ancillary analysis of the NOLGYN study was to evaluate the ability of a machine-learning aglorithm to predict moderate to severe acute postoperative pain based on intraoperative NOL values. ⋯ Our results, even if limited by the small number of patients, suggest that acute postoperative pain is better predicted by a multivariate machine-learning algorithm rather than individual intraoperative nociception variables. Further larger multicentric trials are highly recommended to better understand the relationship between intraoperative nociception and acute postoperative pain. Trial registration Registered on ClinicalTrials.gov in October 2018 (NCT03776838).
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J Clin Monit Comput · Feb 2023
Arterial to end-tidal CO2 gradients during isocapnic hyperventilation.
Isocapnic hyperventilation (ICHV) is occasionally used to maintain the end-expired CO2 partial pressure (PETCO2) when the inspired CO2 (PICO2) rises. Whether maintaining PETCO2 with ICHV during an increase of the PICO2 also maintains arterial PCO2 (PaCO2) remains poorly documented. 12 ASA PS I-II subjects undergoing a robot-assisted radical prostatectomy (RARP) (n = 11) or cystectomy (n = 1) under general endotracheal anesthesia with sevoflurane in O2/air (40% inspired O2) were enrolled. PICO2 was sequentially increased from 0 to 0.5, 1.0, 1.5 and 2% by adding CO2 to the inspiratory limb of the circle system, while increasing ventilation to a target PETCO2 of 4.7-4.9% by adjusting respiratory rate during controlled mechanical ventilation. ⋯ Pa-ETCO2 gradients did not change when PICO2 increased. The effect of a modest rise of PICO2 up to 1.5% on PETCO2 during RARP can be readily overcome by increasing ventilation without altering the Pa-ETCO2 gradients. At higher PICO2, airway pressures may become a limiting factor, which requires further study.
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J Clin Monit Comput · Feb 2023
Pulmonary gas exchange evaluated by machine learning: a computer simulation.
Using computer simulation we investigated whether machine learning (ML) analysis of selected ICU monitoring data can quantify pulmonary gas exchange in multi-compartment format. A 21 compartment ventilation/perfusion (V/Q) model of pulmonary blood flow processed 34,551 combinations of cardiac output, hemoglobin concentration, standard P50, base excess, VO2 and VCO2 plus three model-defining parameters: shunt, log SD and mean V/Q. From these inputs the model produced paired arterial blood gases, first with the inspired O2 fraction (FiO2) adjusted to arterial saturation (SaO2) = 0.90, and second with FiO2 increased by 0.1. 'Stacked regressor' ML ensembles were trained/validated on 90% of this dataset. ⋯ Single-point estimates were less accurate: R2 = 0.77-0.89, slope = 0.991-0.993, intercept = 0.009-0.334. ML applications using blood gas, indirect calorimetry, and cardiac output data can quantify pulmonary gas exchange in terms describing a 20 compartment V/Q model of pulmonary blood flow. High fidelity reports require data from two FiO2 settings.
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J Clin Monit Comput · Feb 2023
Evaluation of machine learning models as decision aids for anesthesiologists.
Machine Learning (ML) models have been developed to predict perioperative clinical parameters. The objective of this study was to determine if ML models can serve as decision aids to improve anesthesiologists' prediction of peak intraoperative glucose values and postoperative opioid requirements. A web-based tool was used to present actual surgical case and patient information to 10 practicing anesthesiologists. ⋯ Feedback questionnaire responses revealed that the anesthesiologist primarily used the ML estimates as reference to modify their clinical judgement. ML models can improve anesthesiologists' estimation of clinical parameters. ML predictions primarily served as reference information that modified an anesthesiologist's clinical estimate.